Caregiver Speech Mixed Models - 7 categories; English and Spanish Separately
Janet Bang
November 17, 2022
This code is for the results to the question, “Do features of caregiver verbal engagement vary as a function of language group and activity?”, but conducts analyses separately per language and for separate levels for all other-child-centered activities (see pre-registration here: https://osf.io/byjfg/).
Load libraries and set theme
library(tidyverse)
library(ggpubr)
library(psych)
library(lme4)
library(lmerTest)
library(emmeans)
library(sjPlot)
library(ggeffects)
theme_set(theme_bw())
get_legend<-function(myggplot){
tmp <- ggplot_gtable(ggplot_build(myggplot))
leg <- which(sapply(tmp$grobs, function(x) x$name) == "guide-box")
legend <- tmp$grobs[[leg]]
return(legend)
}Read in data and convert
# demographics
demo_english <- read_csv("./data_demo_lena_transcripts/demo_english_ms.csv") %>%
rename(id = ID, hi = HI24Final, momed = Momed) %>%
dplyr::select(id, hi, momed) %>%
mutate(id = as.character(id),
language = "english")
demo_spanish <- read_csv("./data_demo_lena_transcripts/demo_spanish_ms.csv") %>%
rename(id = ID, hi = HI_18, momed = MomEd_25m) %>%
dplyr::select(id, hi, momed) %>%
mutate(id = as.character(id),
language = "spanish")
# NOTE about periods of non-tCDCS
# gemods refers to when there are designated start/end periods of other-directed speech (ODS); this was captured using gems (@G) using CHAT conventions
# kwalods refers to when ODS was transcribed at an utterance-level within a tCDS activity period between caregiver and child (e.g., other-directed speech in the background); this was captured per utterances using CHAT postcodes
## for tokens/min and types/min, we do not include ODS that occurred within a period of tCDS, because durations were captured by activity and not by utterance
## for mlu, we include all ODS across gemods and kwalods
# NOTE about speech == "all"
# "speech" includes two levels: all, spont
# all = refers to all speech by caregivers
# spont = refers to only speech by caregivers that was considered spontaneous rather than recited (e.g., reading book text, singing memorized common songs like itsy bitsy spider); therefore, 'spont' is a subset of 'all'
# freq
freq <- read_csv("./data_demo_lena_transcripts/freq.csv") %>%
filter(activity != "kwalods",
speech == "all") %>%
mutate(activity = recode(activity, "gemods" = "non_tcds")) %>%
mutate(id = factor(id),
language = factor(language),
activity = factor(activity, levels = c("books", "play", "food",
"routines", "conv", "ac", "non_tcds")))
# mlu
mlu <- read_csv("./data_demo_lena_transcripts/mlu.csv") %>%
filter(speech == "all") %>%
mutate(activity = recode(activity, "ods" = "non_tcds")) %>%
mutate(id = factor(id),
language = factor(language),
activity = factor(activity, levels = c("books", "play", "food",
"routines", "conv", "ac", "non_tcds")))
# chip
# this includes only caregivers, therefore there is no speaker column
# we exclude periods of ODS because this is about responsiveness to the child during periods of tCDS
chip <- read_csv("./data_demo_lena_transcripts/chip.csv") %>%
filter(activity != "ods") %>%
mutate(id = factor(id),
language = factor(language),
activity = factor(activity, levels = c("books", "play", "food",
"routines", "conv", "ac", "non_tcds")))
str(freq)## spec_tbl_df[,13] [3,308 × 13] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ X1 : num [1:3308] 1 2 3 4 5 6 7 8 9 10 ...
## $ id : Factor w/ 90 levels "7292","7352",..: 47 47 47 47 50 50 52 52 52 52 ...
## $ rectime : num [1:3308] 11923 11923 31360 31360 21499 ...
## $ activity : Factor w/ 7 levels "books","play",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ speaker : chr [1:3308] "CHI" "ADULTS" "CHI" "ADULTS" ...
## $ tokens : num [1:3308] 30 151 35 143 58 588 42 286 33 152 ...
## $ types : num [1:3308] 17 70 17 65 17 199 19 53 17 59 ...
## $ segment_num : num [1:3308] 12 12 15 15 2 2 11 11 5 5 ...
## $ language : Factor w/ 2 levels "english","spanish": 1 1 1 1 1 1 1 1 1 1 ...
## $ speech : chr [1:3308] "all" "all" "all" "all" ...
## $ dur_min : num [1:3308] 3.55 3.55 6.57 6.57 4.71 ...
## $ tokens_permin: num [1:3308] 8.46 42.57 5.32 21.75 12.31 ...
## $ types_permin : num [1:3308] 4.79 19.73 2.59 9.89 3.61 ...
## - attr(*, "spec")=
## .. cols(
## .. X1 = col_double(),
## .. id = col_double(),
## .. rectime = col_double(),
## .. activity = col_character(),
## .. speaker = col_character(),
## .. tokens = col_double(),
## .. types = col_double(),
## .. segment_num = col_double(),
## .. language = col_character(),
## .. speech = col_character(),
## .. dur_min = col_double(),
## .. tokens_permin = col_double(),
## .. types_permin = col_double()
## .. )str(mlu)## spec_tbl_df[,9] [3,002 × 9] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ id : Factor w/ 90 levels "7292","7352",..: 46 46 46 46 46 46 46 46 46 46 ...
## $ activity : Factor w/ 7 levels "books","play",..: 6 6 5 5 7 7 2 2 6 6 ...
## $ speaker : chr [1:3002] "ADULTS" "CHI" "ADULTS" "CHI" ...
## $ segment_num: num [1:3002] 2 2 2 2 2 2 2 2 3 3 ...
## $ words_sum : num [1:3002] 210 66 175 43 11 16 189 47 261 78 ...
## $ num_utt_sum: num [1:3002] 66 35 64 24 2 12 64 28 87 43 ...
## $ mlu_w : num [1:3002] 3.18 1.89 2.73 1.79 5.5 ...
## $ language : Factor w/ 2 levels "english","spanish": 1 1 1 1 1 1 1 1 1 1 ...
## $ speech : chr [1:3002] "all" "all" "all" "all" ...
## - attr(*, "spec")=
## .. cols(
## .. id = col_double(),
## .. activity = col_character(),
## .. speaker = col_character(),
## .. segment_num = col_double(),
## .. words_sum = col_double(),
## .. num_utt_sum = col_double(),
## .. mlu_w = col_double(),
## .. language = col_character(),
## .. speech = col_character()
## .. )str(chip)## spec_tbl_df[,11] [1,118 × 11] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ activity : Factor w/ 7 levels "books","play",..: 6 5 2 6 5 4 6 5 4 2 ...
## $ id : Factor w/ 90 levels "7292","7352",..: 46 46 46 46 46 46 46 46 46 46 ...
## $ rectime : num [1:1118] 15242 15242 15242 14342 14342 ...
## $ total_adult_utt : num [1:1118] 68 64 65 91 43 13 50 8 65 127 ...
## $ total_child_utt : num [1:1118] 46 34 33 54 17 3 14 1 29 49 ...
## $ total_adult_resp : num [1:1118] 62 51 54 77 24 9 30 4 56 106 ...
## $ total_adult_imitexp : num [1:1118] 18 13 15 25 5 2 9 0 16 21 ...
## $ prop_adultresp_outof_childutt : num [1:1118] 1.35 1.5 1.64 1.43 1.41 ...
## $ prop_adult_imitexp_outof_childutt: num [1:1118] 0.391 0.382 0.455 0.463 0.294 ...
## $ language : Factor w/ 2 levels "english","spanish": 1 1 1 1 1 1 1 1 1 1 ...
## $ segment_num : num [1:1118] 2 2 2 3 3 3 4 4 4 5 ...
## - attr(*, "spec")=
## .. cols(
## .. activity = col_character(),
## .. id = col_double(),
## .. rectime = col_double(),
## .. total_adult_utt = col_double(),
## .. total_child_utt = col_double(),
## .. total_adult_resp = col_double(),
## .. total_adult_imitexp = col_double(),
## .. prop_adultresp_outof_childutt = col_double(),
## .. prop_adult_imitexp_outof_childutt = col_double(),
## .. language = col_character(),
## .. segment_num = col_double()
## .. )Create dfs for ADULTS and CHI
# FREQ
freq_adult_en <- freq %>%
filter(speaker == "ADULTS") %>%
filter(language == "english")
freq_adult_sp <- freq %>%
filter(speaker == "ADULTS") %>%
filter(language == "spanish")
# MLU
mlu_adult_en <- mlu %>%
filter(speaker == "ADULTS") %>%
filter(language == "english")
mlu_adult_sp <- mlu %>%
filter(speaker == "ADULTS") %>%
filter(language == "spanish")Prep data for mixed models
Create tokens rate per hour - Children
freq_hr_child <- read_csv("./data_demo_lena_transcripts/freq.csv") %>%
dplyr::select(-X1) %>%
filter(speech == "all",
speaker == "CHI") %>%
group_by(id) %>%
mutate(tokens_sum_child = sum(tokens),
dur_hr = sum(dur_min)/60,
tokens_hr_child = tokens_sum_child/dur_hr) %>%
distinct(id, language, tokens_hr_child) %>%
ungroup() %>%
mutate(id = as.character(id))
# per language
freq_hr_child_en <- freq_hr_child %>% filter(language == "english")
freq_hr_child_sp <- freq_hr_child %>% filter(language == "spanish")Merge freq_adult, child tokens per hour, and demographic info
# freq
freq_all_mm_en <- freq_adult_en %>%
mutate(id = as.character(id)) %>%
left_join(freq_hr_child_en, by = c("id", "language")) %>%
left_join(demo_english, by = c("id", "language"))
freq_all_mm_sp <- freq_adult_sp %>%
mutate(id = as.character(id)) %>%
left_join(freq_hr_child_sp, by = c("id", "language")) %>%
left_join(demo_spanish, by = c("id", "language"))
# mlu
mlu_all_mm_en <- mlu_adult_en %>%
mutate(id = as.character(id)) %>%
left_join(freq_hr_child_en, by = c("id", "language")) %>%
left_join(demo_english, by = c("id", "language"))
mlu_all_mm_sp <- mlu_adult_sp %>%
mutate(id = as.character(id)) %>%
left_join(freq_hr_child_sp, by = c("id", "language")) %>%
left_join(demo_spanish, by = c("id", "language"))
# chip
chip_mm_en <- chip %>%
filter(language == "english") %>%
mutate(id = as.character(id)) %>%
left_join(freq_hr_child_en, by = c("id", "language")) %>%
left_join(demo_english, by = c("id", "language"))
chip_mm_sp <- chip %>%
filter(language == "spanish") %>%
mutate(id = as.character(id)) %>%
left_join(freq_hr_child_sp, by = c("id", "language")) %>%
left_join(demo_spanish, by = c("id", "language"))Mixed models - 7 categories
TOKENS x ACTIVITY - ENGLISH - ALL TALK
# comparing models
m1_english_tokens_all <- lmer(tokens_permin ~ tokens_hr_child +
(1 | id),
data = freq_all_mm_en, REML = F)
m2_english_tokens_all <- lmer(tokens_permin ~ tokens_hr_child + activity +
(1 | id),
data = freq_all_mm_en, REML = F)
# see if adding the intx adds
anova(m1_english_tokens_all, m2_english_tokens_all)## Data: freq_all_mm_en
## Models:
## m1_english_tokens_all: tokens_permin ~ tokens_hr_child + (1 | id)
## m2_english_tokens_all: tokens_permin ~ tokens_hr_child + activity + (1 | id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m1_english_tokens_all 4 8952.9 8971.9 -4472.4 8944.9
## m2_english_tokens_all 10 8796.7 8844.3 -4388.4 8776.7 168.17 6 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# anova table for final model
anova(m2_english_tokens_all)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## tokens_hr_child 18477 18477 1 45.03 12.703 0.0008779 ***
## activity 270623 45104 6 839.48 31.008 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# emmeans
m2_english_tokens_all_emmeans_activity <- emmeans(m2_english_tokens_all, ~ activity)
m2_english_tokens_all_emmeans_activity## activity emmean SE df lower.CL upper.CL
## books 92.5 6.10 678 80.5 104.4
## play 62.3 4.33 369 53.8 70.8
## food 60.6 5.41 594 49.9 71.2
## routines 70.1 5.35 592 59.6 80.6
## conv 74.6 3.88 280 67.0 82.3
## ac 76.0 3.51 203 69.0 82.9
## non_tcds 36.9 3.08 129 30.8 43.0
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95pairs(m2_english_tokens_all_emmeans_activity)## contrast estimate SE df t.ratio p.value
## books - play 30.14 6.95 860 4.337 0.0003
## books - food 31.91 7.63 854 4.184 0.0006
## books - routines 22.34 7.60 854 2.939 0.0525
## books - conv 17.82 6.63 851 2.690 0.1021
## books - ac 16.51 6.48 860 2.548 0.1438
## books - non_tcds 55.53 6.21 854 8.941 <.0001
## play - food 1.77 6.37 858 0.278 1.0000
## play - routines -7.80 6.31 856 -1.236 0.8802
## play - conv -12.32 5.07 848 -2.431 0.1870
## play - ac -13.63 4.80 851 -2.837 0.0695
## play - non_tcds 25.39 4.48 846 5.663 <.0001
## food - routines -9.57 7.06 850 -1.355 0.8252
## food - conv -14.09 6.02 847 -2.342 0.2254
## food - ac -15.40 5.80 849 -2.656 0.1109
## food - non_tcds 23.62 5.54 847 4.264 0.0004
## routines - conv -4.52 5.98 847 -0.756 0.9889
## routines - ac -5.83 5.72 842 -1.018 0.9499
## routines - non_tcds 33.20 5.48 842 6.058 <.0001
## conv - ac -1.31 4.39 841 -0.298 0.9999
## conv - non_tcds 37.71 4.06 836 9.299 <.0001
## ac - non_tcds 39.02 3.71 832 10.531 <.0001
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 7 estimates# plot
tokens_emmeans_en <- data.frame(emmeans(m2_english_tokens_all, ~ activity))
tokens_en <- ggplot(tokens_emmeans_en, aes(activity, emmean, colour = activity)) +
geom_pointrange(aes(ymin = lower.CL, ymax = upper.CL),
position = position_dodge(width = 0.2),
size = 1) +
scale_color_manual(values=c("darkviolet", "firebrick1", "green2", "dodgerblue1", "darkgoldenrod1", "darkgrey", "black"),
name="Activity",
labels=c('Books','Play', 'Food', 'Routines', 'Unst. Conv.', 'Adult-Cent', 'non-tCDS')) +
theme(legend.position = "none",
text = element_text(size = 20),
axis.text.x = element_text(angle = 40, vjust = .9, hjust=.9)) +
scale_x_discrete(labels = c('Books','Play', 'Food', 'Routines', 'Unst. Conv.', 'Adult-Cent', 'non-tCDS')) +
labs(x = "", y = "EMM", title = "English - Tokens (rate per min)")
tokens_en
# model diagnostics
# only takes into account fixed effects, not random effects
plot_model(m2_english_tokens_all, type = "diag") ## [[1]]
##
## [[2]]
## [[2]]$id
##
##
## [[3]]
##
## [[4]]
TOKENS x ACTIVITY - SPANISH - ALL TALK
# comparing models
m1_spanish_tokens_all <- lmer(tokens_permin ~ tokens_hr_child +
(1 | id),
data = freq_all_mm_sp, REML = F)
m2_spanish_tokens_all <- lmer(tokens_permin ~ tokens_hr_child + activity +
(1 | id),
data = freq_all_mm_sp, REML = F)
# see if adding the intx adds
anova(m1_spanish_tokens_all, m2_spanish_tokens_all)## Data: freq_all_mm_sp
## Models:
## m1_spanish_tokens_all: tokens_permin ~ tokens_hr_child + (1 | id)
## m2_spanish_tokens_all: tokens_permin ~ tokens_hr_child + activity + (1 | id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m1_spanish_tokens_all 4 7949.3 7968.0 -3970.6 7941.3
## m2_spanish_tokens_all 10 7851.4 7898.2 -3915.7 7831.4 109.83 6 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# anova table for final model
anova(m2_spanish_tokens_all)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## tokens_hr_child 4435 4434.8 1 43.75 4.1682 0.04725 *
## activity 125494 20915.7 6 763.78 19.6587 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# emmeans
m2_spanish_tokens_all_emmeans_activity <- emmeans(m2_spanish_tokens_all, ~ activity)
m2_spanish_tokens_all_emmeans_activity## activity emmean SE df lower.CL upper.CL
## books 67.4 6.02 627 55.6 79.2
## play 51.0 4.38 379 42.4 59.6
## food 39.4 5.03 509 29.6 49.3
## routines 59.9 4.83 477 50.4 69.3
## conv 57.2 3.73 241 49.9 64.6
## ac 57.2 3.25 149 50.7 63.6
## non_tcds 31.2 2.92 101 25.4 37.0
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95pairs(m2_spanish_tokens_all_emmeans_activity)## contrast estimate SE df t.ratio p.value
## books - play 16.4460 6.81 782 2.416 0.1933
## books - food 27.9722 7.31 787 3.826 0.0027
## books - routines 7.5614 7.15 784 1.058 0.9400
## books - conv 10.1976 6.43 785 1.585 0.6921
## books - ac 10.2591 6.20 790 1.654 0.6472
## books - non_tcds 36.2247 5.98 785 6.055 <.0001
## play - food 11.5261 5.98 775 1.926 0.4633
## play - routines -8.8846 5.78 768 -1.536 0.7228
## play - conv -6.2485 4.91 769 -1.272 0.8648
## play - ac -6.1870 4.57 772 -1.355 0.8256
## play - non_tcds 19.7786 4.33 767 4.573 0.0001
## food - routines -20.4108 6.31 772 -3.237 0.0214
## food - conv -17.7746 5.50 770 -3.234 0.0216
## food - ac -17.7131 5.17 770 -3.424 0.0115
## food - non_tcds 8.2525 4.98 770 1.657 0.6453
## routines - conv 2.6362 5.29 762 0.499 0.9989
## routines - ac 2.6976 5.00 769 0.540 0.9982
## routines - non_tcds 28.6632 4.78 765 6.000 <.0001
## conv - ac 0.0615 3.95 768 0.016 1.0000
## conv - non_tcds 26.0271 3.67 763 7.093 <.0001
## ac - non_tcds 25.9656 3.17 761 8.183 <.0001
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 7 estimates# plot
tokens_emmeans_sp <- data.frame(emmeans(m2_spanish_tokens_all, ~ activity))
tokens_sp <- ggplot(tokens_emmeans_sp, aes(activity, emmean, colour = activity)) +
geom_pointrange(aes(ymin = lower.CL, ymax = upper.CL),
position = position_dodge(width = 0.2),
size = 1) +
scale_color_manual(values=c("darkviolet", "firebrick1", "green2", "dodgerblue1", "darkgoldenrod1", "darkgrey", "black"),
name="Activity",
labels=c('Books','Play', 'Food', 'Routines', 'Unst. Conv.', 'Adult-Cent', 'non-tCDS')) +
theme(legend.position = "none",
text = element_text(size = 20),
axis.text.x = element_text(angle = 40, vjust = .9, hjust=.9)) +
scale_x_discrete(labels = c('Books','Play', 'Food', 'Routines', 'Unst. Conv.', 'Adult-Cent', 'non-tCDS')) +
labs(x = "", y = "EMM", title = "Spanish - Tokens (rate per min)")
tokens_sp
# model diagnostics
# only takes into account fixed effects, not random effects
plot_model(m2_spanish_tokens_all, type = "diag") ## [[1]]
##
## [[2]]
## [[2]]$id
##
##
## [[3]]
##
## [[4]]
TYPES x ACTIVITY - ENGLISH - ALL TALK
# comparing models
m1_english_types_all <- lmer(types_permin ~ tokens_hr_child +
(1 | id),
data = freq_all_mm_en, REML = F)
m2_english_types_all <- lmer(types_permin ~ tokens_hr_child + activity +
(1 | id),
data = freq_all_mm_en, REML = F)
# see if adding the intx adds
anova(m1_english_types_all, m2_english_types_all)## Data: freq_all_mm_en
## Models:
## m1_english_types_all: types_permin ~ tokens_hr_child + (1 | id)
## m2_english_types_all: types_permin ~ tokens_hr_child + activity + (1 | id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m1_english_types_all 4 8516.0 8535.0 -4254.0 8508.0
## m2_english_types_all 10 8411.8 8459.4 -4195.9 8391.8 116.17 6 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# anova table for final model
anova(m2_english_types_all)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## tokens_hr_child 10350 10350 1 44.8 10.794 0.001982 **
## activity 119808 19968 6 849.3 20.825 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# emmeans
m2_english_types_all_emmeans_activity <- emmeans(m2_english_types_all, ~ activity)
m2_english_types_all_emmeans_activity## activity emmean SE df lower.CL upper.CL
## books 29.3 4.74 770 20.0 38.6
## play 27.1 3.25 539 20.7 33.5
## food 34.6 4.18 732 26.4 42.8
## routines 38.9 4.13 742 30.8 47.0
## conv 39.4 2.87 452 33.7 45.0
## ac 52.5 2.54 338 47.5 57.5
## non_tcds 20.6 2.15 210 16.4 24.9
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95pairs(m2_english_types_all_emmeans_activity)## contrast estimate SE df t.ratio p.value
## books - play 2.213 5.59 870 0.396 0.9997
## books - food -5.323 6.15 866 -0.866 0.9774
## books - routines -9.617 6.12 867 -1.570 0.7015
## books - conv -10.048 5.34 864 -1.881 0.4937
## books - ac -23.162 5.21 870 -4.446 0.0002
## books - non_tcds 8.674 5.00 866 1.733 0.5937
## play - food -7.536 5.12 869 -1.471 0.7621
## play - routines -11.830 5.08 868 -2.327 0.2322
## play - conv -12.261 4.09 861 -2.998 0.0442
## play - ac -25.375 3.87 863 -6.550 <.0001
## play - non_tcds 6.461 3.62 858 1.784 0.5592
## food - routines -4.294 5.70 863 -0.754 0.9890
## food - conv -4.725 4.86 860 -0.973 0.9598
## food - ac -17.839 4.68 862 -3.814 0.0028
## food - non_tcds 13.997 4.47 860 3.129 0.0299
## routines - conv -0.431 4.83 859 -0.089 1.0000
## routines - ac -13.545 4.63 853 -2.927 0.0541
## routines - non_tcds 18.291 4.43 854 4.130 0.0008
## conv - ac -13.114 3.55 851 -3.690 0.0044
## conv - non_tcds 18.722 3.28 843 5.702 <.0001
## ac - non_tcds 31.836 3.00 838 10.603 <.0001
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 7 estimates# plot
types_emmeans_en <- data.frame(emmeans(m2_english_types_all, ~ activity))
types_en <- ggplot(types_emmeans_en, aes(activity, emmean, colour = activity)) +
geom_pointrange(aes(ymin = lower.CL, ymax = upper.CL),
position = position_dodge(width = 0.2),
size = 1) +
scale_color_manual(values=c("darkviolet", "firebrick1", "green2", "dodgerblue1", "darkgoldenrod1", "darkgrey", "black")) +
theme(legend.position = "none",
text = element_text(size = 20),
axis.text.x = element_text(angle = 40, vjust = .9, hjust=.9)) +
scale_x_discrete(labels = c('Books','Play', 'Food', 'Routines', 'Unst. Conv.', 'Adult-Cent', 'non-tCDS')) +
labs(x = "", y = "EMM", title = "English - Types (rate per min)")
types_en
# model diagnostics
# only takes into account fixed effects, not random effects
plot_model(m2_english_types_all, type = "diag")## [[1]]
##
## [[2]]
## [[2]]$id
##
##
## [[3]]
##
## [[4]]
TYPES x ACTIVITY - SPANISH - ALL TALK
# comparing models
m1_spanish_types_all <- lmer(types_permin ~ tokens_hr_child +
(1 | id),
data = freq_all_mm_sp, REML = F)
m2_spanish_types_all <- lmer(types_permin ~ tokens_hr_child + activity +
(1 | id),
data = freq_all_mm_sp, REML = F)
# see if adding the intx adds
anova(m1_spanish_types_all, m2_spanish_types_all)## Data: freq_all_mm_sp
## Models:
## m1_spanish_types_all: types_permin ~ tokens_hr_child + (1 | id)
## m2_spanish_types_all: types_permin ~ tokens_hr_child + activity + (1 | id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m1_spanish_types_all 4 7583.5 7602.2 -3787.7 7575.5
## m2_spanish_types_all 10 7530.2 7577.0 -3755.1 7510.2 65.261 6 3.816e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# anova table for final model
anova(m2_spanish_types_all)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## tokens_hr_child 2480 2480.4 1 42.34 3.3511 0.0742 .
## activity 50490 8414.9 6 774.64 11.3691 3.34e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# emmeans
m2_spanish_types_all_emmeans_activity <- emmeans(m2_spanish_types_all, ~ activity)
m2_spanish_types_all_emmeans_activity## activity emmean SE df lower.CL upper.CL
## books 22.9 4.74 717 13.6 32.2
## play 21.3 3.33 608 14.8 27.9
## food 19.4 3.91 696 11.7 27.0
## routines 31.4 3.73 690 24.1 38.7
## conv 30.6 2.74 452 25.2 36.0
## ac 38.0 2.27 281 33.6 42.5
## non_tcds 18.2 1.94 178 14.4 22.0
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95pairs(m2_spanish_types_all_emmeans_activity)## contrast estimate SE df t.ratio p.value
## books - play 1.624 5.62 798 0.289 1.0000
## books - food 3.595 6.01 800 0.598 0.9969
## books - routines -8.430 5.89 799 -1.431 0.7847
## books - conv -7.672 5.30 800 -1.448 0.7753
## books - ac -15.076 5.10 800 -2.959 0.0496
## books - non_tcds 4.745 4.93 799 0.963 0.9617
## play - food 1.971 4.95 791 0.398 0.9997
## play - routines -10.054 4.80 782 -2.095 0.3563
## play - conv -9.296 4.08 782 -2.281 0.2546
## play - ac -16.700 3.78 786 -4.413 0.0002
## play - non_tcds 3.122 3.59 779 0.870 0.9769
## food - routines -12.025 5.22 787 -2.302 0.2443
## food - conv -11.267 4.56 785 -2.473 0.1705
## food - ac -18.671 4.29 784 -4.352 0.0003
## food - non_tcds 1.150 4.13 784 0.279 1.0000
## routines - conv 0.758 4.40 772 0.172 1.0000
## routines - ac -6.646 4.14 782 -1.604 0.6801
## routines - non_tcds 13.175 3.97 776 3.320 0.0163
## conv - ac -7.404 3.27 781 -2.261 0.2645
## conv - non_tcds 12.417 3.05 772 4.071 0.0010
## ac - non_tcds 19.821 2.64 768 7.509 <.0001
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 7 estimates# plot
types_emmeans_sp <- data.frame(emmeans(m2_spanish_types_all, ~ activity))
types_sp <- ggplot(types_emmeans_sp, aes(activity, emmean, colour = activity)) +
geom_pointrange(aes(ymin = lower.CL, ymax = upper.CL),
position = position_dodge(width = 0.2),
size = 1) +
scale_color_manual(values=c("darkviolet", "firebrick1", "green2", "dodgerblue1", "darkgoldenrod1", "darkgrey", "black")) +
theme(legend.position = "none",
text = element_text(size = 20),
axis.text.x = element_text(angle = 40, vjust = .9, hjust=.9)) +
scale_x_discrete(labels = c('Books','Play', 'Food', 'Routines', 'Unst. Conv.', 'Adult-Cent', 'non-tCDS')) +
labs(x = "", y = "EMM", title = "Spanish - Types (rate per min)")
types_sp
# model diagnostics
# only takes into account fixed effects, not random effects
plot_model(m2_spanish_types_all, type = "diag")## [[1]]
##
## [[2]]
## [[2]]$id
##
##
## [[3]]
##
## [[4]]
MLUw x ACTIVITY - ENGLISH - ALL TALK
# comparing models
m1_english_mlu_all <- lmer(mlu_w ~ tokens_hr_child +
(1 | id),
data = mlu_all_mm_en, REML = F)
m2_english_mlu_all <- lmer(mlu_w ~ tokens_hr_child + activity +
(1 | id),
data = mlu_all_mm_en, REML = F)
# see if adding the intx adds
anova(m1_english_mlu_all, m2_english_mlu_all)## Data: mlu_all_mm_en
## Models:
## m1_english_mlu_all: mlu_w ~ tokens_hr_child + (1 | id)
## m2_english_mlu_all: mlu_w ~ tokens_hr_child + activity + (1 | id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m1_english_mlu_all 4 2444.0 2462.8 -1218.0 2436.0
## m2_english_mlu_all 10 2354.3 2401.1 -1167.1 2334.3 101.78 6 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# anova table for final model
anova(m2_english_mlu_all)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## tokens_hr_child 13.439 13.439 1 44.46 13.410 0.0006624 ***
## activity 108.789 18.131 6 773.32 18.092 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# emmeans
m2_english_mlu_all_emmeans_activity <- emmeans(m2_english_mlu_all, ~ activity)
m2_english_mlu_all_emmeans_activity## activity emmean SE df lower.CL upper.CL
## books 4.80 0.1658 548 4.48 5.13
## play 3.53 0.1211 270 3.30 3.77
## food 3.56 0.1480 456 3.27 3.85
## routines 3.70 0.1464 451 3.41 3.99
## conv 3.75 0.1095 199 3.54 3.97
## ac 3.51 0.1010 150 3.31 3.71
## non_tcds 4.24 0.0963 126 4.05 4.44
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95pairs(m2_english_mlu_all_emmeans_activity)## contrast estimate SE df t.ratio p.value
## books - play 1.2703 0.184 792 6.905 <.0001
## books - food 1.2445 0.201 786 6.180 <.0001
## books - routines 1.1056 0.201 786 5.510 <.0001
## books - conv 1.0509 0.175 784 6.009 <.0001
## books - ac 1.2923 0.172 792 7.529 <.0001
## books - non_tcds 0.5601 0.168 788 3.338 0.0154
## play - food -0.0258 0.168 789 -0.153 1.0000
## play - routines -0.1647 0.167 787 -0.987 0.9569
## play - conv -0.2194 0.134 780 -1.639 0.6569
## play - ac 0.0220 0.127 783 0.173 1.0000
## play - non_tcds -0.7102 0.123 780 -5.757 <.0001
## food - routines -0.1389 0.186 781 -0.746 0.9896
## food - conv -0.1936 0.159 779 -1.221 0.8859
## food - ac 0.0478 0.153 782 0.312 0.9999
## food - non_tcds -0.6844 0.150 780 -4.571 0.0001
## routines - conv -0.0547 0.158 779 -0.347 0.9999
## routines - ac 0.1867 0.151 775 1.236 0.8800
## routines - non_tcds -0.5456 0.148 777 -3.677 0.0047
## conv - ac 0.2414 0.116 775 2.078 0.3668
## conv - non_tcds -0.4908 0.112 774 -4.374 0.0003
## ac - non_tcds -0.7322 0.103 766 -7.094 <.0001
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 7 estimates# plot
mlu_emmeans_en <- data.frame(emmeans(m2_english_mlu_all, ~ activity))
mlu_en <- ggplot(mlu_emmeans_en, aes(activity, emmean, colour = activity)) +
geom_pointrange(aes(ymin = lower.CL, ymax = upper.CL),
position = position_dodge(width = 0.2),
size = 1) +
scale_color_manual(values=c("darkviolet", "firebrick1", "green2", "dodgerblue1", "darkgoldenrod1", "darkgrey", "black")) +
theme(legend.position = "none",
text = element_text(size = 20),
axis.text.x = element_text(angle = 40, vjust = .9, hjust=.9)) +
scale_x_discrete(labels = c('Books','Play', 'Food', 'Routines', 'Unst. Conv.', 'Adult-Cent', 'non-tCDS')) +
labs(x = "", y = "EMM", title = "English - MLUw")
mlu_en
# model diagnostics
# only takes into account fixed effects, not random effects
plot_model(m2_english_mlu_all, type = "diag")## [[1]]
##
## [[2]]
## [[2]]$id
##
##
## [[3]]
##
## [[4]]
MLUw x ACTIVITY - SPANISH - ALL TALK
# comparing models
m1_spanish_mlu_all <- lmer(mlu_w ~ tokens_hr_child +
(1 | id),
data = mlu_all_mm_sp, REML = F)
m2_spanish_mlu_all <- lmer(mlu_w ~ tokens_hr_child + activity +
(1 | id),
data = mlu_all_mm_sp, REML = F)
# see if adding the intx adds
anova(m1_spanish_mlu_all, m2_spanish_mlu_all)## Data: mlu_all_mm_sp
## Models:
## m1_spanish_mlu_all: mlu_w ~ tokens_hr_child + (1 | id)
## m2_spanish_mlu_all: mlu_w ~ tokens_hr_child + activity + (1 | id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m1_spanish_mlu_all 4 2049.0 2067.3 -1020.50 2041.0
## m2_spanish_mlu_all 10 1951.3 1997.2 -965.67 1931.3 109.65 6 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# anova table for final model
anova(m2_spanish_mlu_all)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## tokens_hr_child 6.343 6.3433 1 42.16 8.3497 0.006069 **
## activity 90.147 15.0246 6 696.37 19.7768 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# emmeans
m2_spanish_mlu_all_emmeans_activity <- emmeans(m2_spanish_mlu_all, ~ activity)
m2_spanish_mlu_all_emmeans_activity## activity emmean SE df lower.CL upper.CL
## books 3.75 0.1653 531 3.42 4.07
## play 2.81 0.1218 298 2.57 3.05
## food 2.79 0.1388 411 2.52 3.07
## routines 3.03 0.1333 381 2.77 3.30
## conv 2.97 0.1051 189 2.76 3.18
## ac 2.74 0.0933 123 2.55 2.92
## non_tcds 3.55 0.0901 108 3.37 3.73
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95pairs(m2_spanish_mlu_all_emmeans_activity)## contrast estimate SE df t.ratio p.value
## books - play 0.9381 0.1831 714 5.125 <.0001
## books - food 0.9511 0.1969 719 4.830 <.0001
## books - routines 0.7115 0.1924 716 3.698 0.0044
## books - conv 0.7746 0.1733 718 4.471 0.0002
## books - ac 1.0058 0.1676 722 6.003 <.0001
## books - non_tcds 0.1930 0.1644 718 1.174 0.9037
## play - food 0.0130 0.1606 707 0.081 1.0000
## play - routines -0.2267 0.1550 700 -1.463 0.7668
## play - conv -0.1635 0.1317 701 -1.242 0.8775
## play - ac 0.0677 0.1229 705 0.551 0.9980
## play - non_tcds -0.7451 0.1201 702 -6.203 <.0001
## food - routines -0.2396 0.1691 704 -1.417 0.7927
## food - conv -0.1765 0.1473 703 -1.198 0.8949
## food - ac 0.0547 0.1389 702 0.394 0.9997
## food - non_tcds -0.7581 0.1370 702 -5.534 <.0001
## routines - conv 0.0631 0.1416 695 0.446 0.9994
## routines - ac 0.2944 0.1341 701 2.194 0.2998
## routines - non_tcds -0.5185 0.1317 699 -3.937 0.0018
## conv - ac 0.2312 0.1061 701 2.180 0.3078
## conv - non_tcds -0.5816 0.1029 696 -5.652 <.0001
## ac - non_tcds -0.8128 0.0905 694 -8.979 <.0001
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 7 estimates# plot
mlu_emmeans_sp <- data.frame(emmeans(m2_spanish_mlu_all, ~ activity))
mlu_sp <- ggplot(mlu_emmeans_sp, aes(activity, emmean, colour = activity)) +
geom_pointrange(aes(ymin = lower.CL, ymax = upper.CL),
position = position_dodge(width = 0.2),
size = 1) +
scale_color_manual(values=c("darkviolet", "firebrick1", "green2", "dodgerblue1", "darkgoldenrod1", "darkgrey", "black")) +
theme(legend.position = "none",
text = element_text(size = 20),
axis.text.x = element_text(angle = 40, vjust = .9, hjust=.9)) +
scale_x_discrete(labels = c('Books','Play', 'Food', 'Routines', 'Unst. Conv.', 'Adult-Cent', 'non-tCDS')) +
labs(x = "", y = "EMM", title = "Spanish - MLUw")
mlu_sp
# model diagnostics
# only takes into account fixed effects, not random effects
plot_model(m2_spanish_mlu_all, type = "diag")## [[1]]
##
## [[2]]
## [[2]]$id
##
##
## [[3]]
##
## [[4]]
PROP RESPONSES x ACTIVITY - ENGLISH - ALL TALK
# comparing models
m1_english_propresp_all <- lmer(prop_adultresp_outof_childutt ~ tokens_hr_child +
(1 | id),
data = chip_mm_en, REML = F)
m2_english_propresp_all <- lmer(prop_adultresp_outof_childutt ~ tokens_hr_child + activity +
(1 | id),
data = chip_mm_en, REML = F)
# see if adding the intx adds
anova(m1_english_propresp_all, m2_english_propresp_all)## Data: chip_mm_en
## Models:
## m1_english_propresp_all: prop_adultresp_outof_childutt ~ tokens_hr_child + (1 | id)
## m2_english_propresp_all: prop_adultresp_outof_childutt ~ tokens_hr_child + activity +
## m2_english_propresp_all: (1 | id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m1_english_propresp_all 4 1368.4 1385.6 -680.20 1360.4
## m2_english_propresp_all 9 1359.0 1397.7 -670.51 1341.0 19.387 5 0.001628 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# anova table for final model
anova(m2_english_propresp_all)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## tokens_hr_child 11.782 11.7816 1 48.74 17.5775 0.0001158 ***
## activity 13.260 2.6519 5 540.72 3.9565 0.0015620 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# emmeans
m2_english_propresp_all_emmeans_activity <- emmeans(m2_english_propresp_all, ~ activity)
m2_english_propresp_all_emmeans_activity## activity emmean SE df lower.CL upper.CL
## books 2.44 0.1259 465 2.19 2.69
## play 2.02 0.0870 334 1.85 2.19
## food 1.89 0.1127 448 1.67 2.11
## routines 1.86 0.1132 463 1.64 2.08
## conv 1.91 0.0775 289 1.75 2.06
## ac 1.86 0.0736 251 1.72 2.01
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95pairs(m2_english_propresp_all_emmeans_activity)## contrast estimate SE df t.ratio p.value
## books - play 0.41950 0.149 548 2.808 0.0576
## books - food 0.55247 0.165 550 3.355 0.0109
## books - routines 0.58229 0.166 550 3.517 0.0063
## books - conv 0.53418 0.143 549 3.737 0.0028
## books - ac 0.57586 0.142 546 4.046 0.0008
## play - food 0.13298 0.139 550 0.959 0.9305
## play - routines 0.16280 0.139 550 1.171 0.8504
## play - conv 0.11469 0.111 546 1.035 0.9061
## play - ac 0.15636 0.108 547 1.448 0.6976
## food - routines 0.02982 0.156 550 0.191 1.0000
## food - conv -0.01829 0.132 547 -0.139 1.0000
## food - ac 0.02339 0.130 550 0.180 1.0000
## routines - conv -0.04811 0.132 547 -0.363 0.9992
## routines - ac -0.00643 0.130 546 -0.049 1.0000
## conv - ac 0.04168 0.101 545 0.414 0.9984
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 6 estimates# plot
propresp_emmeans_en <- data.frame(emmeans(m2_english_propresp_all, ~ activity))
resp_en <- ggplot(propresp_emmeans_en, aes(activity, emmean, colour = activity)) +
geom_pointrange(aes(ymin = lower.CL, ymax = upper.CL),
position = position_dodge(width = 0.2),
size = 1) +
scale_color_manual(values=c("darkviolet", "firebrick1", "green2", "dodgerblue1", "darkgoldenrod1", "darkgrey")) +
theme(legend.position = "none",
text = element_text(size = 20),
axis.text.x = element_text(angle = 40, vjust = .9, hjust=.9)) +
scale_x_discrete(labels = c('Books','Play', 'Food', 'Routines', 'Unst. Conv.', 'Adult-Cent')) +
labs(x = "", y = "EMM", title = "English - Proportion of Responses")
resp_en
# model diagnostics
# only takes into account fixed effects, not random effects
plot_model(m2_english_propresp_all, type = "diag")## [[1]]
##
## [[2]]
## [[2]]$id
##
##
## [[3]]
##
## [[4]]
PROP RESPONSES x ACTIVITY - SPANISH - ALL TALK
# comparing models
m1_spanish_propresp_all <- lmer(prop_adultresp_outof_childutt ~ tokens_hr_child +
(1 | id),
data = chip_mm_sp, REML = F)
m2_spanish_propresp_all <- lmer(prop_adultresp_outof_childutt ~ tokens_hr_child + activity +
(1 | id),
data = chip_mm_sp, REML = F)
# see if adding the intx adds
anova(m1_spanish_propresp_all, m2_spanish_propresp_all)## Data: chip_mm_sp
## Models:
## m1_spanish_propresp_all: prop_adultresp_outof_childutt ~ tokens_hr_child + (1 | id)
## m2_spanish_propresp_all: prop_adultresp_outof_childutt ~ tokens_hr_child + activity +
## m2_spanish_propresp_all: (1 | id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m1_spanish_propresp_all 4 1060.9 1077.5 -526.43 1052.9
## m2_spanish_propresp_all 9 1045.0 1082.5 -513.52 1027.0 25.837 5 9.596e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# anova table for final model
anova(m2_spanish_propresp_all)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## tokens_hr_child 4.6205 4.6205 1 38.88 10.1463 0.002848 **
## activity 12.1288 2.4258 5 450.48 5.3268 9.077e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# emmeans
m2_spanish_propresp_all_emmeans_activity <- emmeans(m2_spanish_propresp_all, ~ activity)
m2_spanish_propresp_all_emmeans_activity## activity emmean SE df lower.CL upper.CL
## books 2.15 0.1298 379 1.90 2.41
## play 1.90 0.0982 258 1.70 2.09
## food 1.78 0.1091 318 1.56 1.99
## routines 1.94 0.1064 314 1.74 2.15
## conv 1.91 0.0839 170 1.74 2.07
## ac 1.60 0.0753 118 1.45 1.75
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95pairs(m2_spanish_propresp_all_emmeans_activity)## contrast estimate SE df t.ratio p.value
## books - play 0.2566 0.1458 465 1.761 0.4925
## books - food 0.3762 0.1564 474 2.406 0.1563
## books - routines 0.2092 0.1532 468 1.365 0.7477
## books - conv 0.2449 0.1376 469 1.780 0.4798
## books - ac 0.5520 0.1342 475 4.112 0.0007
## play - food 0.1195 0.1294 461 0.924 0.9403
## play - routines -0.0475 0.1261 452 -0.376 0.9990
## play - conv -0.0117 0.1082 454 -0.108 1.0000
## play - ac 0.2954 0.1023 460 2.888 0.0464
## food - routines -0.1670 0.1352 457 -1.235 0.8195
## food - conv -0.1312 0.1182 458 -1.110 0.8773
## food - ac 0.1758 0.1121 457 1.569 0.6192
## routines - conv 0.0358 0.1143 443 0.313 0.9996
## routines - ac 0.3428 0.1098 452 3.124 0.0232
## conv - ac 0.3071 0.0882 455 3.481 0.0072
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 6 estimates# plot
propresp_emmeans_sp <- data.frame(emmeans(m2_spanish_propresp_all, ~ activity))
resp_sp <- ggplot(propresp_emmeans_sp, aes(activity, emmean, colour = activity)) +
geom_pointrange(aes(ymin = lower.CL, ymax = upper.CL),
position = position_dodge(width = 0.2),
size = 1) +
scale_color_manual(values=c("darkviolet", "firebrick1", "green2", "dodgerblue1", "darkgoldenrod1", "darkgrey")) +
theme(legend.position = "none",
text = element_text(size = 20),
axis.text.x = element_text(angle = 40, vjust = .9, hjust=.9)) +
scale_x_discrete(labels = c('Books','Play', 'Food', 'Routines', 'Unst. Conv.', 'Adult-Cent')) +
labs(x = "", y = "EMM", title = "Spanish - Proportion of Responses")
resp_sp
# model diagnostics
# only takes into account fixed effects, not random effects
plot_model(m2_spanish_propresp_all, type = "diag")## [[1]]
##
## [[2]]
## [[2]]$id
##
##
## [[3]]
##
## [[4]]
PROP IMIT/EXP x ACTIVITY - ENGLISH - ALL TALK; no sig main effect of activity
# comparing models
m1_english_propimitexp_all <- lmer(prop_adult_imitexp_outof_childutt ~ tokens_hr_child +
(1 | id),
data = chip_mm_en, REML = F)
m2_english_propimitexp_all <- lmer(prop_adult_imitexp_outof_childutt ~ tokens_hr_child + activity +
(1 | id),
data = chip_mm_en, REML = F)
# see if adding the intx adds
anova(m1_english_propimitexp_all, m2_english_propimitexp_all)## Data: chip_mm_en
## Models:
## m1_english_propimitexp_all: prop_adult_imitexp_outof_childutt ~ tokens_hr_child + (1 | id)
## m2_english_propimitexp_all: prop_adult_imitexp_outof_childutt ~ tokens_hr_child + activity +
## m2_english_propimitexp_all: (1 | id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m1_english_propimitexp_all 4 430.04 447.23 -211.02 422.04
## m2_english_propimitexp_all 9 431.88 470.56 -206.94 413.88 8.1564 5 0.1478# anova table for final model
anova(m2_english_propimitexp_all)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## tokens_hr_child 0.15738 0.15738 1 42.25 1.2786 0.2645
## activity 1.01328 0.20266 5 540.18 1.6465 0.1459# emmeans
m2_english_propimitexp_all_emmeans_activity <- emmeans(m2_english_propimitexp_all, ~ activity)
m2_english_propimitexp_all_emmeans_activity## activity emmean SE df lower.CL upper.CL
## books 0.505 0.0532 468 0.400 0.609
## play 0.409 0.0365 355 0.337 0.481
## food 0.360 0.0476 456 0.267 0.454
## routines 0.353 0.0478 473 0.259 0.447
## conv 0.385 0.0324 317 0.321 0.449
## ac 0.347 0.0307 276 0.286 0.407
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95# plot
propimitexp_emmeans_en <- data.frame(emmeans(m2_english_propimitexp_all, ~ activity))
imit_exp_en <- ggplot(propimitexp_emmeans_en, aes(activity, emmean, colour = activity)) +
geom_pointrange(aes(ymin = lower.CL, ymax = upper.CL),
position = position_dodge(width = 0.2),
size = 1) +
scale_color_manual(values=c("darkviolet", "firebrick1", "green2", "dodgerblue1", "darkgoldenrod1", "darkgrey")) +
theme(legend.position = "none",
text = element_text(size = 20),
axis.text.x = element_text(angle = 40, vjust = .9, hjust=.9)) +
scale_x_discrete(labels = c('Books','Play', 'Food', 'Routines', 'Unst. Conv.', 'Adult-Cent')) +
labs(x = "", y = "EMM", title = "English - Proportion of Imitations/Expansions")
imit_exp_en
# model diagnostics
# only takes into account fixed effects, not random effects
plot_model(m2_english_propimitexp_all, type = "diag")## [[1]]
##
## [[2]]
## [[2]]$id
##
##
## [[3]]
##
## [[4]]
PROP IMIT/EXP x ACTIVITY - SPANISH - ALL TALK; no sig main effect of activity
# comparing models
m1_spanish_propimitexp_all <- lmer(prop_adult_imitexp_outof_childutt ~ tokens_hr_child +
(1 | id),
data = chip_mm_sp, REML = F)
m2_spanish_propimitexp_all <- lmer(prop_adult_imitexp_outof_childutt ~ tokens_hr_child + activity +
(1 | id),
data = chip_mm_sp, REML = F)
# see if adding the intx adds
anova(m1_spanish_propimitexp_all, m2_spanish_propimitexp_all)## Data: chip_mm_sp
## Models:
## m1_spanish_propimitexp_all: prop_adult_imitexp_outof_childutt ~ tokens_hr_child + (1 | id)
## m2_spanish_propimitexp_all: prop_adult_imitexp_outof_childutt ~ tokens_hr_child + activity +
## m2_spanish_propimitexp_all: (1 | id)
## npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
## m1_spanish_propimitexp_all 4 268.52 285.17 -130.26 260.52
## m2_spanish_propimitexp_all 9 271.29 308.74 -126.64 253.29 7.2343 5 0.2038# anova table for final model
anova(m2_spanish_propimitexp_all)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## tokens_hr_child 0.03168 0.031682 1 46.44 0.3457 0.5594
## activity 0.67092 0.134185 5 459.82 1.4642 0.2002# emmeans
m2_spanish_propimitexp_all_emmeans_activity <- emmeans(m2_spanish_propimitexp_all, ~ activity)
m2_spanish_propimitexp_all_emmeans_activity## activity emmean SE df lower.CL upper.CL
## books 0.376 0.0561 400 0.265 0.486
## play 0.393 0.0417 307 0.311 0.475
## food 0.343 0.0468 362 0.251 0.435
## routines 0.442 0.0456 366 0.352 0.531
## conv 0.376 0.0349 209 0.308 0.445
## ac 0.321 0.0308 142 0.261 0.382
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95# plot
propimitexp_emmeans_sp <- data.frame(emmeans(m2_spanish_propimitexp_all, ~ activity))
imit_exp_sp <- ggplot(propimitexp_emmeans_sp, aes(activity, emmean, colour = activity)) +
geom_pointrange(aes(ymin = lower.CL, ymax = upper.CL),
position = position_dodge(width = 0.2),
size = 1) +
scale_color_manual(values=c("darkviolet", "firebrick1", "green2", "dodgerblue1", "darkgoldenrod1", "darkgrey")) +
theme(legend.position = "none",
text = element_text(size = 20),
axis.text.x = element_text(angle = 40, vjust = .9, hjust=.9)) +
scale_x_discrete(labels = c('Books','Play', 'Food', 'Routines', 'Unst. Conv.', 'Adult-Cent')) +
labs(x = "", y = "EMM", title = "Spanish - Proportion of Imitations/Expansions")
imit_exp_sp
# model diagnostics
# only takes into account fixed effects, not random effects
plot_model(m2_spanish_propimitexp_all, type = "diag")## [[1]]
##
## [[2]]
## [[2]]$id
##
##
## [[3]]
##
## [[4]]
Combining plots
# grid all
ggarrange(tokens_en, types_en, mlu_en, resp_en, imit_exp_en, common.legend = T, legend = "bottom")
ggarrange(tokens_sp, types_sp, mlu_sp, resp_sp, imit_exp_sp, common.legend = T, legend = "bottom")